Dynamic Classification of Time-Series Categorical Data

2. The method of claim 1, wherein the classifying each of the plurality of individuals includes comparing the count vector to a set of ground truth models associated with a set of known classes of individuals including the known class of individuals.

5. The method of claim 4, wherein the squeezing the expanded context vector and the re-classifying are performed only after receiving a threshold amount of the additional position data.

6. The method of claim 1, wherein the context vector is normalized over time.

7. The method of claim 6, wherein the normalization over time includes selecting one of the text strings for a given unit of time.

8. The method of claim 1, wherein the categories of locations include at least one of at home, moving, and at work.

9. The method of claim 8, wherein the known class of individuals includes at least one of works from home, works a full day in an office, works morning hours in the office, and works afternoon hours in the office.

10. The method of claim 1, wherein the categories of locations include at least one of moving by train and moving by automobile.

11. The method of claim 1, wherein the at least one factor includes at least one of a routing algorithm when providing directions to the plurality of people, a quantity of buses running on a given route, a frequency of trains running on a given line, a bus fare for the given route, a train fare for the given route, and a toll on a given road.

13. The non-transitory computer-readable media of claim 12, wherein the classifying each of the plurality of individuals includes comparing the count vector to a set of ground truth models associated with a set of known classes of individuals including the known class of individuals.

16. The non-transitory computer-readable media of claim 15, wherein the squeezing the expanded context vector and the re-classifying are performed only after receiving a threshold amount of the additional position data.

17. The non-transitory computer-readable media of claim 12, wherein the context vector is normalized over time.

18. The non-transitory computer-readable media of claim 17, wherein the normalization over time includes selecting one of the text strings for a given unit of time.

19. The non-transitory computer-readable media of claim 12, wherein the categories of locations include at least one of at home, moving, and at work.

20. The non-transitory computer-readable media of claim 12, wherein the at least one factor includes at least one of a routing algorithm when providing directions to the plurality of people, a quantity of buses running on a given route, a frequency of trains running on a given line, a bus fare for the given route, a train fare for the given route, and a toll on a given road.